The goal of this script is to generate a Seurat object for sample GSM3717035.
LogNormalize, then doublets
detection using scran hybrid and scDblFinder
method (but not filtering)LogNormalize, for only the remaining
cellsPCAtSNE and UMAPlibrary(dplyr)
library(patchwork)
library(ggplot2)
.libPaths()
## [1] "/usr/local/lib/R/library"
In this section, we set the global settings of the analysis. We will store data there :
out_dir = "."
We load the parameters :
sample_name = params$sample_name # "GSM3717034"
# sample_name = "GSM3717034"
Input count matrix is there :
count_matrix_dir = paste0(out_dir, "/input/", sample_name, "/")
count_matrix_file = list.files(count_matrix_dir, full.names = TRUE)
count_matrix_file
## [1] "./input/GSM3717035//GSM3717035_DS2-Rie_5_comb_clean.dge.txt.gz"
We load the markers and specific colors for each cell type :
cell_markers = readRDS(paste0(out_dir, "/../../1_metadata/hs_hd_cell_markers.rds"))
cell_markers = lapply(cell_markers, FUN = toupper)
lengths(cell_markers)
## CD4 T cells CD8 T cells Langerhans cells macrophages
## 13 13 9 10
## B cells cuticle cortex medulla
## 16 15 16 10
## IRS proliferative IBL ORS
## 16 20 15 16
## IFE HFSC melanocytes sebocytes
## 17 17 10 8
Here are custom colors for each cell type :
color_markers = readRDS(paste0(out_dir, "/../../1_metadata/hs_hd_color_markers.rds"))
data.frame(cell_type = names(color_markers),
color = unlist(color_markers)) %>%
ggplot2::ggplot(., aes(x = cell_type, y = 0, fill = cell_type)) +
ggplot2::geom_point(pch = 21, size = 5) +
ggplot2::scale_fill_manual(values = unlist(color_markers), breaks = names(color_markers)) +
ggplot2::theme_classic() +
ggplot2::theme(legend.position = "none",
axis.line = element_blank(),
axis.title = element_blank(),
axis.ticks = element_blank(),
axis.text.y = element_blank())
We load markers to display on the dotplot :
dotplot_markers = readRDS(paste0(out_dir, "/../../1_metadata/hs_hd_dotplot_markers.rds"))
dotplot_markers = lapply(dotplot_markers, FUN = toupper)
dotplot_markers
## $`CD4 T cells`
## [1] "CD3E" "CD4"
##
## $`CD8 T cells`
## [1] "CD3E" "CD8A"
##
## $`Langerhans cells`
## [1] "CD207" "CPVL"
##
## $macrophages
## [1] "TREM2" "MSR1"
##
## $`B cells`
## [1] "CD79A" "CD79B"
##
## $cuticle
## [1] "KRT32" "KRT35"
##
## $cortex
## [1] "KRT31" "PRR9"
##
## $medulla
## [1] "BAMBI" "ADLH1A3"
##
## $IRS
## [1] "KRT71" "KRT73"
##
## $proliferative
## [1] "TOP2A" "MCM5"
##
## $IBL
## [1] "KRT16" "KRT6C"
##
## $ORS
## [1] "KRT15" "GPX2"
##
## $IFE
## [1] "SPINK5" "LY6D"
##
## $HFSC
## [1] "DIO2" "TCEAL2"
##
## $melanocytes
## [1] "DCT" "MLANA"
##
## $sebocytes
## [1] "CLMP" "PPARG"
We load metadata for this sample :
sample_info = readRDS(paste0(out_dir, "/../1_metadata/takahashi_sample_info.rds"))
sample_info %>%
dplyr::filter(project_name == sample_name)
## project_name sample_type sample_identifier color
## 1 GSM3717035 Takahashi_HD Takahashi_HD_2 palegreen3
These is a parameter for different functions :
cl = aquarius::create_parallel_instance(nthreads = 3L)
cut_log_nCount_RNA = 0.5 # almost no filter
cut_nFeature_RNA = 20 # almost no filter
cut_percent.mt = 20
cut_percent.rb = 50
In this section, we load the count matrix.
mat = read.table(count_matrix_file,
header = TRUE, row.names = 1)
# For the two 10X data, this is required
rownames(mat) = stringr::str_remove(rownames(mat),
pattern = "hg19_")
# Seurat object
sobj = Seurat::CreateSeuratObject(counts = mat,
project = sample_name,
assay = "RNA")
rm(mat)
sobj
## An object of class Seurat
## 14154 features across 3000 samples within 1 assay
## Active assay: RNA (14154 features, 0 variable features)
(Time to run : 19.13 s)
We add the same columns as in metadata :
row_oi = (sample_info$project_name == sample_name)
sobj$project_name = sample_name
sobj$sample_identifier = sample_info[row_oi, "sample_identifier"]
sobj$sample_type = sample_info[row_oi, "sample_type"]
colnames(sobj@meta.data)
## [1] "orig.ident" "nCount_RNA" "nFeature_RNA"
## [4] "project_name" "sample_identifier" "sample_type"
sobj = Seurat::NormalizeData(sobj,
normalization.method = "LogNormalize",
assay = "RNA")
sobj = Seurat::FindVariableFeatures(sobj,
assay = "RNA",
nfeatures = 3000)
sobj
## An object of class Seurat
## 14154 features across 3000 samples within 1 assay
## Active assay: RNA (14154 features, 3000 variable features)
We generate a tSNE to visualize cells before filtering.
sobj = aquarius::dimensions_reduction(sobj = sobj,
assay = "RNA",
reduction = "pca",
max_dims = 100,
verbose = FALSE)
Seurat::ElbowPlot(sobj, ndims = 100, reduction = "RNA_pca")
We generate a tSNE with 20 principal components :
ndims = 20
sobj = Seurat::RunTSNE(sobj,
reduction = "RNA_pca",
dims = 1:ndims,
seed.use = 1337L,
reduction.name = paste0("RNA_pca_", ndims, "_tsne"),
check_duplicates = FALSE)
sobj
## An object of class Seurat
## 14154 features across 3000 samples within 1 assay
## Active assay: RNA (14154 features, 3000 variable features)
## 2 dimensional reductions calculated: RNA_pca, RNA_pca_20_tsne
We annotate cells for cell type using
Seurat::AddModuleScore function.
sobj = aquarius::cell_annot_custom(sobj,
newname = "cell_type",
markers = cell_markers,
use_negative = TRUE,
add_score = TRUE,
verbose = TRUE)
colnames(sobj@meta.data) = stringr::str_replace_all(string = colnames(sobj@meta.data),
pattern = " ",
replacement = "_")
sobj$cell_type = factor(sobj$cell_type, levels = names(cell_markers))
table(sobj$cell_type)
##
## CD4 T cells CD8 T cells Langerhans cells macrophages
## 283 147 132 236
## B cells cuticle cortex medulla
## 146 152 218 87
## IRS proliferative IBL ORS
## 449 95 317 90
## IFE HFSC melanocytes sebocytes
## 323 218 62 45
(Time to run : 1.6 s)
To justify cell type annotation, we can make a dotplot :
markers = c("PTPRC", "MSX2", "KRT16",
unique(unlist(dotplot_markers[levels(sobj$cell_type)])))
markers = markers[markers %in% rownames(sobj)]
aquarius::plot_dotplot(sobj, assay = "RNA",
column_name = "cell_type",
markers = markers,
nb_hline = 0) +
ggplot2::scale_color_gradientn(colors = aquarius:::color_gene) +
ggplot2::theme(legend.position = "right",
legend.box = "vertical",
legend.direction = "vertical",
axis.title = element_blank(),
axis.text = element_text(size = 15))
We can make a barplot to see the composition of each dataset, and visualize cell types on the projection.
df_proportion = as.data.frame(prop.table(table(sobj$orig.ident,
sobj$cell_type)))
colnames(df_proportion) = c("orig.ident", "cell_type", "freq")
quantif = table(sobj$orig.ident) %>%
as.data.frame.table() %>%
`colnames<-`(c("orig.ident", "nb_cells"))
# Plot
plot_list = list()
plot_list[[2]] = aquarius::plot_barplot(df = df_proportion,
x = "orig.ident",
y = "freq",
fill = "cell_type",
position = ggplot2::position_fill()) +
ggplot2::scale_fill_manual(name = "Cell type",
values = color_markers[levels(df_proportion$cell_type)],
breaks = levels(df_proportion$cell_type)) +
ggplot2::geom_label(data = quantif, inherit.aes = FALSE,
aes(x = orig.ident, y = 1.05, label = nb_cells),
label.size = 0)
plot_list[[1]] = Seurat::DimPlot(sobj, group.by = "cell_type") +
ggplot2::scale_color_manual(values = unlist(color_markers),
breaks = names(color_markers)) +
ggplot2::labs(title = sample_name,
subtitle = paste0(ncol(sobj), " cells")) +
Seurat::NoLegend() + Seurat::NoAxes() +
ggplot2::theme(aspect.ratio = 1,
plot.title = element_text(hjust = 0.5),
plot.subtitle = element_text(hjust = 0.5))
patchwork::wrap_plots(plot_list, nrow = 1, widths = c(6, 1))
We annotate cells for cell cycle phase using Seurat and
cyclone.
cc_columns = aquarius::add_cell_cycle(sobj = sobj,
assay = "RNA",
species_rdx = "hs",
BPPARAM = cl)@meta.data[, c("Seurat.Phase", "Phase")]
##
## G1 G2M S
## 940 467 250
sobj$Seurat.Phase = cc_columns$Seurat.Phase
sobj$cyclone.Phase = cc_columns$Phase
table(sobj$Seurat.Phase, sobj$cyclone.Phase)
##
## G1 G2M S
## G1 594 300 179
## G2M 130 97 19
## S 208 68 48
## Undecided 8 2 4
(Time to run : 86.35 s)
We visualize cell cycle on the projection :
plot_list = list()
plot_list[[2]] = Seurat::DimPlot(sobj, group.by = "Seurat.Phase") +
ggplot2::labs(title = "Cell Cycle Phase",
subtitle = "Seurat.Phase") +
Seurat::NoLegend() + Seurat::NoAxes() +
ggplot2::theme(aspect.ratio = 1,
plot.title = element_text(hjust = 0.5),
plot.subtitle = element_text(hjust = 0.5))
plot_list[[1]] = Seurat::DimPlot(sobj, group.by = "cyclone.Phase") +
ggplot2::labs(title = "Cell Cycle Phase",
subtitle = "cyclone.Phase") +
Seurat::NoLegend() + Seurat::NoAxes() +
ggplot2::theme(aspect.ratio = 1,
plot.title = element_text(hjust = 0.5),
plot.subtitle = element_text(hjust = 0.5))
patchwork::wrap_plots(plot_list, nrow = 1)
In this section, we look at the number of genes expressed by each cell, the number of UMI, the percentage of mitochondrial genes expressed, and the percentage of ribosomal genes expressed. Then, without taking into account the cells expressing low number of genes or have low number of UMI, we identify doublet cells.
We compute four quality metrics :
sobj = Seurat::PercentageFeatureSet(sobj, pattern = "^MT", col.name = "percent.mt")
sobj = Seurat::PercentageFeatureSet(sobj, pattern = "^RP[L|S][0-9]*$", col.name = "percent.rb")
sobj$log_nCount_RNA = log(sobj$nCount_RNA)
head(sobj@meta.data)
## orig.ident nCount_RNA nFeature_RNA project_name sample_identifier
## CGCCTTTCATAN GSM3717035 7964 2884 GSM3717035 Takahashi_HD_2
## CCGATCTCACAC GSM3717035 7793 969 GSM3717035 Takahashi_HD_2
## CCCAAACAGATT GSM3717035 7624 903 GSM3717035 Takahashi_HD_2
## CGGCTGCCCCCT GSM3717035 7643 996 GSM3717035 Takahashi_HD_2
## AGACCGAGTTTN GSM3717035 4920 1785 GSM3717035 Takahashi_HD_2
## GACCCCAATCAG GSM3717035 4244 1473 GSM3717035 Takahashi_HD_2
## sample_type score_CD4_T_cells score_CD8_T_cells
## CGCCTTTCATAN Takahashi_HD -0.060786334 -0.10749758
## CCGATCTCACAC Takahashi_HD -0.008414737 -0.01734410
## CCCAAACAGATT Takahashi_HD -0.009489926 -0.01056253
## CGGCTGCCCCCT Takahashi_HD -0.027721726 -0.03993688
## AGACCGAGTTTN Takahashi_HD -0.030631658 -0.04983103
## GACCCCAATCAG Takahashi_HD -0.032577105 -0.04436121
## score_Langerhans_cells score_macrophages score_B_cells
## CGCCTTTCATAN -0.089738708 -0.08712277 -0.064563009
## CCGATCTCACAC -0.008255792 -0.01884102 -0.014136631
## CCCAAACAGATT -0.019287144 -0.01895848 -0.007943879
## CGGCTGCCCCCT -0.033984782 -0.01828739 -0.015756914
## AGACCGAGTTTN -0.044375763 -0.03895486 -0.023674138
## GACCCCAATCAG -0.049715417 -0.04209497 -0.027864071
## score_cuticle score_cortex score_medulla score_IRS
## CGCCTTTCATAN -0.36007656 -0.35696629 -0.166183080 -0.5677918
## CCGATCTCACAC -0.04351531 -0.13247372 -0.097834707 -0.1815798
## CCCAAACAGATT -0.09153740 -0.10755914 -0.008269825 -0.1124703
## CGGCTGCCCCCT -0.09514875 -0.17754031 -0.009496449 -0.2058987
## AGACCGAGTTTN 0.78519184 -0.01570155 0.100411609 -0.4369413
## GACCCCAATCAG 0.16335476 1.24804933 0.082050198 -0.3791192
## score_proliferative score_IBL score_ORS score_IFE score_HFSC
## CGCCTTTCATAN 0.78200394 -0.76174782 -0.35506045 -0.8087253 -0.39698867
## CCGATCTCACAC -0.06577908 -0.11763828 0.02900355 -0.2087845 -0.07089822
## CCCAAACAGATT -0.05166447 -0.17494148 -0.02324349 -0.1394793 -0.11734190
## CGGCTGCCCCCT -0.06361383 -0.05869937 -0.09354266 -0.2678832 -0.04011047
## AGACCGAGTTTN -0.18763834 -0.30476376 -0.25607052 -0.4834545 -0.17633970
## GACCCCAATCAG -0.16271881 -0.51856714 -0.13245522 -0.4738363 -0.05939946
## score_melanocytes score_sebocytes cell_type Seurat.Phase
## CGCCTTTCATAN 1.356694229 -0.19599798 proliferative S
## CCGATCTCACAC -0.120452125 0.06530365 ORS G1
## CCCAAACAGATT -0.115849022 -0.08074175 medulla G1
## CGGCTGCCCCCT -0.138167505 -0.07608291 IBL G1
## AGACCGAGTTTN -0.316872916 0.03795329 cuticle G1
## GACCCCAATCAG 0.006357349 -0.19736448 cortex G1
## cyclone.Phase percent.mt percent.rb log_nCount_RNA
## CGCCTTTCATAN G1 2.8628830 10.321447 8.982687
## CCGATCTCACAC G1 0.4362890 1.886308 8.960981
## CCCAAACAGATT G1 0.4459601 1.875656 8.939056
## CGGCTGCCCCCT G1 0.8504514 2.551354 8.941545
## AGACCGAGTTTN G1 2.7235772 18.475610 8.501064
## GACCCCAATCAG G1 1.8614515 15.386428 8.353261
We get the cell barcodes for the failing cells :
fail_percent.mt = sobj@meta.data %>% dplyr::filter(percent.mt > cut_percent.mt) %>% rownames()
fail_percent.rb = sobj@meta.data %>% dplyr::filter(percent.rb > cut_percent.rb) %>% rownames()
fail_log_nCount_RNA = sobj@meta.data %>% dplyr::filter(log_nCount_RNA < cut_log_nCount_RNA) %>% rownames()
fail_nFeature_RNA = sobj@meta.data %>% dplyr::filter(nFeature_RNA < cut_nFeature_RNA) %>% rownames()
Without taking into account the low UMI and low number of features cells, we identify doublets.
fsobj = subset(sobj, invert = TRUE,
cells = unique(c(fail_log_nCount_RNA, fail_nFeature_RNA)))
fsobj
## An object of class Seurat
## 14154 features across 2857 samples within 1 assay
## Active assay: RNA (14154 features, 3000 variable features)
## 2 dimensional reductions calculated: RNA_pca, RNA_pca_20_tsne
On this filtered dataset, we apply doublet cells detection. Just before, we run the normalization, taking into account only the remaining cells.
fsobj = Seurat::NormalizeData(fsobj,
normalization.method = "LogNormalize",
assay = "RNA")
fsobj = Seurat::FindVariableFeatures(fsobj,
assay = "RNA",
nfeatures = 3000)
fsobj
## An object of class Seurat
## 14154 features across 2857 samples within 1 assay
## Active assay: RNA (14154 features, 3000 variable features)
## 2 dimensional reductions calculated: RNA_pca, RNA_pca_20_tsne
We identify doublet cells :
fsobj = aquarius::find_doublets(sobj = fsobj,
BPPARAM = cl)
## [1] 14154 2857
##
## FALSE TRUE
## 2097 760
## [12:02:53] WARNING: amalgamation/../src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
##
## FALSE TRUE
## 2640 217
##
## FALSE TRUE
## 2083 774
fail_doublets_consensus = Seurat::WhichCells(fsobj, expression = doublets_consensus.class)
fail_doublets_scDblFinder = Seurat::WhichCells(fsobj, expression = scDblFinder.class)
fail_doublets_hybrid = Seurat::WhichCells(fsobj, expression = hybrid_score.class)
(Time to run : 47.92 s)
We add the information in the non filtered Seurat object :
sobj$doublets_consensus.class = dplyr::case_when(!(colnames(sobj) %in% colnames(fsobj)) ~ NA,
colnames(sobj) %in% fail_doublets_consensus ~ TRUE,
!(colnames(sobj) %in% fail_doublets_consensus) ~ FALSE)
sobj$scDblFinder.class = dplyr::case_when(!(colnames(sobj) %in% colnames(fsobj)) ~ NA,
colnames(sobj) %in% fail_doublets_scDblFinder ~ TRUE,
!(colnames(sobj) %in% fail_doublets_scDblFinder) ~ FALSE)
sobj$hybrid_score.class = dplyr::case_when(!(colnames(sobj) %in% colnames(fsobj)) ~ NA,
colnames(sobj) %in% fail_doublets_hybrid ~ TRUE,
!(colnames(sobj) %in% fail_doublets_hybrid) ~ FALSE)
We can visualize the 4 cells quality with a Venn diagram :
n_filtered = c(fail_percent.mt, fail_percent.rb, fail_log_nCount_RNA, fail_nFeature_RNA) %>%
unique() %>% length()
percent_filtered = round(100*(n_filtered/ncol(sobj)), 2)
ggvenn::ggvenn(list(percent.mt = fail_percent.mt,
percent.rb = fail_percent.rb,
log_nCount_RNA = fail_log_nCount_RNA,
nFeature_RNA = fail_nFeature_RNA),
fill_color = c("#0073C2FF", "#EFC000FF", "orange", "pink"),
stroke_size = 0.5, set_name_size = 4) +
ggplot2::labs(title = "Filtered out cells",
subtitle = paste0(n_filtered, " cells (", percent_filtered, " % of all cells)")) +
ggplot2::theme(plot.title = element_text(hjust = 0.5, face = "bold"),
plot.subtitle = element_text(hjust = 0.5))
To visualize the threshold for number of UMI, we can make a histogram :
aquarius::plot_qc_density(df = sobj@meta.data,
x = "log_nCount_RNA",
bins = 200,
group_by = "orig.ident",
group_color = setNames(sample_info$color,
nm = sample_info$sample_identifiant),
x_thresh = cut_log_nCount_RNA)
Seurat::VlnPlot(sobj, features = "log_nCount_RNA", pt.size = 0.001,
group.by = "cell_type", cols = color_markers) +
ggplot2::scale_fill_manual(values = color_markers, breaks = names(color_markers)) +
ggplot2::geom_hline(yintercept = cut_log_nCount_RNA, col = "red") +
ggplot2::labs(x = "")
sobj$fail = ifelse(colnames(sobj) %in% fail_log_nCount_RNA,
yes = as.character(sobj$cell_type), no = NA)
sobj$fail = factor(sobj$fail, levels = c(levels(sobj$cell_type), NA))
Seurat::DimPlot(sobj, group.by = "fail", na.value = "gray80", cols = color_markers) +
ggplot2::labs(title = "log_nCount_RNA",
subtitle = paste0(length(fail_log_nCount_RNA), " cells")) +
Seurat::NoAxes() +
ggplot2::theme(aspect.ratio = 1,
plot.title = element_text(hjust = 0.5),
plot.subtitle = element_text(hjust = 0.5))
To visualize the threshold for number of features, we can make a histogram :
aquarius::plot_qc_density(df = sobj@meta.data,
x = "nFeature_RNA",
bins = 200,
group_by = "orig.ident",
group_color = setNames(sample_info$color,
nm = sample_info$sample_identifiant),
x_thresh = cut_nFeature_RNA)
Seurat::VlnPlot(sobj, features = "nFeature_RNA", pt.size = 0.001,
group.by = "cell_type", cols = color_markers) +
ggplot2::scale_fill_manual(values = color_markers, breaks = names(color_markers)) +
ggplot2::geom_hline(yintercept = cut_nFeature_RNA, col = "red") +
ggplot2::labs(x = "")
sobj$fail = ifelse(colnames(sobj) %in% fail_nFeature_RNA,
yes = as.character(sobj$cell_type), no = NA)
sobj$fail = factor(sobj$fail, levels = c(levels(sobj$cell_type), NA))
Seurat::DimPlot(sobj, group.by = "fail", na.value = "gray80", cols = color_markers) +
ggplot2::labs(title = "nFeature_RNA",
subtitle = paste0(length(fail_nFeature_RNA), " cells")) +
Seurat::NoAxes() +
ggplot2::theme(aspect.ratio = 1,
plot.title = element_text(hjust = 0.5),
plot.subtitle = element_text(hjust = 0.5))
To identify a threshold for mitochondrial gene expression, we can make a histogram :
aquarius::plot_qc_density(df = sobj@meta.data,
x = "percent.mt",
bins = 200,
group_by = "orig.ident",
group_color = setNames(sample_info$color,
nm = sample_info$sample_identifiant),
x_thresh = cut_percent.mt)
Seurat::VlnPlot(sobj, features = "percent.mt", pt.size = 0.001,
group.by = "cell_type", cols = color_markers) +
ggplot2::scale_fill_manual(values = color_markers, breaks = names(color_markers)) +
ggplot2::geom_hline(yintercept = cut_percent.mt, col = "red") +
ggplot2::labs(x = "")
sobj$fail = ifelse(colnames(sobj) %in% fail_percent.mt,
yes = as.character(sobj$cell_type), no = NA)
sobj$fail = factor(sobj$fail, levels = c(levels(sobj$cell_type), NA))
Seurat::DimPlot(sobj, group.by = "fail", na.value = "gray80", cols = color_markers) +
ggplot2::labs(title = "percent.mt",
subtitle = paste0(length(fail_percent.mt), " cells")) +
Seurat::NoAxes() +
ggplot2::theme(aspect.ratio = 1,
plot.title = element_text(hjust = 0.5),
plot.subtitle = element_text(hjust = 0.5))
To identify a threshold for ribosomal gene expression, we can make a histogram :
aquarius::plot_qc_density(df = sobj@meta.data,
x = "percent.rb",
bins = 200,
group_by = "orig.ident",
group_color = setNames(sample_info$color,
nm = sample_info$sample_identifiant),
x_thresh = cut_percent.rb)
Seurat::VlnPlot(sobj, features = "percent.rb", pt.size = 0.001,
group.by = "cell_type", cols = color_markers) +
ggplot2::scale_fill_manual(values = color_markers, breaks = names(color_markers)) +
ggplot2::geom_hline(yintercept = cut_percent.rb, col = "red") +
ggplot2::labs(x = "")
sobj$fail = ifelse(colnames(sobj) %in% fail_percent.rb,
yes = as.character(sobj$cell_type), no = NA)
sobj$fail = factor(sobj$fail, levels = c(levels(sobj$cell_type), NA))
Seurat::DimPlot(sobj, group.by = "fail", na.value = "gray80", cols = color_markers) +
ggplot2::labs(title = "percent.rb",
subtitle = paste0(length(fail_percent.rb), " cells")) +
Seurat::NoAxes() +
ggplot2::theme(aspect.ratio = 1,
plot.title = element_text(hjust = 0.5),
plot.subtitle = element_text(hjust = 0.5))
We would like to see if the number of feature expressed by cell, and
the number of UMI is correlated with the cell type, the percentage of
mitochondrial and ribosomal gene expressed, and the doublet status. We
build the log_nCount_RNA by nFeature_RNA
figure, where cells (dots) are colored by these different metrics.
This is the figure, colored by cell type :
aquarius::plot_qc_facslike(df = sobj@meta.data,
x = "nFeature_RNA",
y = "log_nCount_RNA",
col_by = "cell_type",
col_colors = unname(color_markers),
x_thresh = cut_nFeature_RNA,
y_thresh = cut_log_nCount_RNA,
bins = 200)
This is the figure, colored by the percentage of mitochondrial genes expressed in cell :
aquarius::plot_qc_facslike(df = sobj@meta.data,
x = "nFeature_RNA",
y = "log_nCount_RNA",
col_by = "percent.mt",
x_thresh = cut_nFeature_RNA,
y_thresh = cut_log_nCount_RNA,
bins = 200)
This is the figure, colored by the percentage of ribosomal genes expressed in cell :
aquarius::plot_qc_facslike(df = sobj@meta.data,
x = "nFeature_RNA",
y = "log_nCount_RNA",
col_by = "percent.rb",
x_thresh = cut_nFeature_RNA,
y_thresh = cut_log_nCount_RNA,
bins = 200)
This is the figure, colored by the doublet cells status
(doublets_consensus.class) :
aquarius::plot_qc_facslike(df = sobj@meta.data,
x = "nFeature_RNA",
y = "log_nCount_RNA",
col_by = "doublets_consensus.class",
col_colors = setNames(nm = c(TRUE, FALSE),
aquarius::gg_color_hue(2)),
x_thresh = cut_nFeature_RNA,
y_thresh = cut_log_nCount_RNA,
bins = 200)
This is the figure, colored by the doublet cells status
(scDblFinder.class) :
aquarius::plot_qc_facslike(df = sobj@meta.data,
x = "nFeature_RNA",
y = "log_nCount_RNA",
col_by = "scDblFinder.class",
col_colors = setNames(nm = c(TRUE, FALSE),
aquarius::gg_color_hue(2)),
x_thresh = cut_nFeature_RNA,
y_thresh = cut_log_nCount_RNA,
bins = 200)
This is the figure, colored by the doublet cells status
(hybrid_score.class) :
aquarius::plot_qc_facslike(df = sobj@meta.data,
x = "nFeature_RNA",
y = "log_nCount_RNA",
col_by = "hybrid_score.class",
col_colors = setNames(nm = c(TRUE, FALSE),
aquarius::gg_color_hue(2)),
x_thresh = cut_nFeature_RNA,
y_thresh = cut_log_nCount_RNA,
bins = 200)
Do filtered cells belong to a particular cell type ?
sobj$all_cells = TRUE
plot_list = list()
## All cells
df = sobj@meta.data
if (nrow(df) == 0) {
plot_list[[1]] = ggplot()
} else {
plot_list[[1]] = aquarius::plot_piechart(df = df,
logical_var = "all_cells",
grouping_var = "cell_type",
colors = color_markers,
display_legend = TRUE) +
ggplot2::labs(title = "All cells",
subtitle = paste(nrow(df), "cells")) +
ggplot2::theme(plot.title = element_text(hjust = 0.5, face = "bold"),
plot.subtitle = element_text(hjust = 0.5))
}
## Doublets consensus
df = sobj@meta.data %>%
dplyr::filter(doublets_consensus.class)
if (nrow(df) == 0) {
plot_list[[2]] = ggplot()
} else {
plot_list[[2]] = aquarius::plot_piechart(df = df,
logical_var = "all_cells",
grouping_var = "cell_type",
colors = color_markers,
display_legend = TRUE) +
ggplot2::labs(title = "doublets_consensus.class",
subtitle = paste(sum(sobj$doublets_consensus.class, na.rm = TRUE), "cells")) +
ggplot2::theme(plot.title = element_text(hjust = 0.5, face = "bold"),
plot.subtitle = element_text(hjust = 0.5))
}
## percent.mt
df = sobj@meta.data %>%
dplyr::filter(percent.mt > cut_percent.mt)
if (nrow(df) == 0) {
plot_list[[3]] = ggplot()
} else {
plot_list[[3]] = aquarius::plot_piechart(df = df,
logical_var = "all_cells",
grouping_var = "cell_type",
colors = color_markers,
display_legend = TRUE) +
ggplot2::labs(title = paste("percent.mt >", cut_percent.mt),
subtitle = paste(length(fail_percent.mt), "cells")) +
ggplot2::theme(plot.title = element_text(hjust = 0.5, face = "bold"),
plot.subtitle = element_text(hjust = 0.5))
}
## percent.rb
df = sobj@meta.data %>%
dplyr::filter(percent.rb > cut_percent.rb)
if (nrow(df) == 0) {
plot_list[[4]] = ggplot()
} else {
plot_list[[4]] = aquarius::plot_piechart(df = df,
logical_var = "all_cells",
grouping_var = "cell_type",
colors = color_markers,
display_legend = TRUE) +
ggplot2::labs(title = paste("percent.rb >", cut_percent.rb),
subtitle = paste(length(fail_percent.rb), "cells")) +
ggplot2::theme(plot.title = element_text(hjust = 0.5, face = "bold"),
plot.subtitle = element_text(hjust = 0.5))
}
## log_nCount_RNA
df = sobj@meta.data %>%
dplyr::filter(log_nCount_RNA < cut_log_nCount_RNA)
if (nrow(df) == 0) {
plot_list[[5]] = ggplot()
} else {
plot_list[[5]] = aquarius::plot_piechart(df = df,
logical_var = "all_cells",
grouping_var = "cell_type",
colors = color_markers,
display_legend = TRUE) +
ggplot2::labs(title = paste("log_nCount_RNA <", round(cut_log_nCount_RNA, 2)),
subtitle = paste(length(fail_log_nCount_RNA), "cells")) +
ggplot2::theme(plot.title = element_text(hjust = 0.5, face = "bold"),
plot.subtitle = element_text(hjust = 0.5))
}
## nFeature_RNA
df = sobj@meta.data %>%
dplyr::filter(nFeature_RNA < cut_nFeature_RNA)
if (nrow(df) == 0) {
plot_list[[6]] = ggplot()
} else {
plot_list[[6]] = aquarius::plot_piechart(df = df,
logical_var = "all_cells",
grouping_var = "cell_type",
colors = color_markers,
display_legend = TRUE) +
ggplot2::labs(title = paste("nFeature_RNA <", round(cut_nFeature_RNA, 2)),
subtitle = paste(length(fail_nFeature_RNA), "cells")) +
ggplot2::theme(plot.title = element_text(hjust = 0.5, face = "bold"),
plot.subtitle = element_text(hjust = 0.5))
}
patchwork::wrap_plots(plot_list, ncol = 3) +
patchwork::plot_layout(guides = "collect") &
ggplot2::theme(legend.position = "right")
We can compare doublet detection methods with a Venn diagram :
ggvenn::ggvenn(list(hybrid = fail_doublets_hybrid,
scDblFinder = fail_doublets_scDblFinder),
fill_color = c("#0073C2FF", "#EFC000FF"),
stroke_size = 0.5, set_name_size = 4) +
ggplot2::ggtitle(label = "Doublet cells") +
ggplot2::theme(plot.title = element_text(hjust = 0.5, face = "bold"))
We visualize cells annotation for doublets :
plot_list = list()
# scDblFinder.class
sobj$fail = ifelse(sobj$scDblFinder.class,
yes = as.character(sobj$cell_type), no = NA)
sobj$fail = factor(sobj$fail, levels = c(levels(sobj$cell_type), NA))
plot_list[[1]] = Seurat::DimPlot(sobj, group.by = "fail",
na.value = "gray80", cols = color_markers) +
ggplot2::labs(title = "scDblFinder.class",
subtitle = paste0(sum(sobj$scDblFinder.class, na.rm = TRUE), " cells")) +
Seurat::NoAxes() + Seurat::NoLegend() +
ggplot2::theme(aspect.ratio = 1,
plot.title = element_text(hjust = 0.5),
plot.subtitle = element_text(hjust = 0.5))
# hybrid_score.class
sobj$fail = ifelse(sobj$hybrid_score.class,
yes = as.character(sobj$cell_type), no = NA)
sobj$fail = factor(sobj$fail, levels = c(levels(sobj$cell_type), NA))
plot_list[[2]] = Seurat::DimPlot(sobj, group.by = "fail",
na.value = "gray80", cols = color_markers) +
ggplot2::labs(title = "hybrid_score.class",
subtitle = paste0(sum(sobj$hybrid_score.class, na.rm = TRUE), " cells")) +
Seurat::NoAxes() +
ggplot2::theme(aspect.ratio = 1,
plot.title = element_text(hjust = 0.5),
plot.subtitle = element_text(hjust = 0.5))
sobj$fail = NULL
# Plot
patchwork::wrap_plots(plot_list, nrow = 1)
What is the composition of doublet cells ? We just look at score for each cell type.
sobj$orig.ident.doublets = case_when(is.na(sobj$doublets_consensus.class) ~ "bad quality",
sobj$doublets_consensus.class == TRUE ~ paste0(sobj$orig.ident, " doublets"),
sobj$doublets_consensus.class == FALSE ~ "not doublet")
sobj$orig.ident.doublets = factor(sobj$orig.ident.doublets,
levels = c(paste0(as.character(sample_info$sample_identifiant), " doublets"),
"bad quality", "not doublet"))
doublets_compo = function(score1, score2) {
type1 = unlist(lapply(stringr::str_split(score1, pattern = "score_"), `[[`, 2))
type2 = unlist(lapply(stringr::str_split(score2, pattern = "score_"), `[[`, 2))
if (type1 == type2) {
the_title = "Homotypic doublet"
the_subtitle = type1
score1 = "log_nCount_RNA"
} else {
the_title = "Heterotypic doublet"
the_subtitle = paste(type1, type2, sep = " + ")
}
p = sobj@meta.data %>%
dplyr::arrange(desc(orig.ident.doublets)) %>%
ggplot2::ggplot(., aes(x = eval(parse(text = score1)),
y = eval(parse(text = score2)),
col = orig.ident.doublets)) +
ggplot2::geom_point(size = 0.25) +
ggplot2::scale_color_manual(values = c(sample_info$color, "gray90", "gray60"),
breaks = c(paste0(as.character(sample_info$sample_identifiant), " doublets"),
"bad quality", "not doublet")) +
ggplot2::labs(x = score1, y = score2,
title = the_title, subtitle = the_subtitle) +
ggplot2::theme_classic() +
ggplot2::theme(aspect.ratio = 1,
plot.title = element_text(hjust = 0.5),
plot.subtitle = element_text(hjust = 0.5))
return(p)
}
score_columns = grep(x = colnames(sobj@meta.data),
pattern = "^score",
value = TRUE)
combinations = expand.grid(score_columns, score_columns) %>%
apply(., 1, sort) %>% t() %>%
as.data.frame()
combinations = combinations[!duplicated(combinations), ]
plot_list = apply(combinations, 1, FUN = function(elem) {
doublets_compo(elem[1], elem[2])
})
sobj$orig.ident.doublets = NULL
patchwork::wrap_plots(plot_list, ncol = 4) +
patchwork::plot_layout(guides = "collect") &
ggplot2::theme(legend.position = "right")
We could save this object before filtering (remove
eval = FALSE) :
saveRDS(sobj, paste0(out_dir, "/datasets/", sample_name, "_sobj_unfiltered.rds"))
We remove :
Note: We do not filter cells detected as doublets. Indeed, few genes and transcripts are detected per cell, and the best cells are therefore annotated as doublets.
sobj = subset(sobj, invert = TRUE,
cells = unique(c(fail_log_nCount_RNA, fail_nFeature_RNA,
fail_percent.mt, fail_percent.rb)))
sobj
## An object of class Seurat
## 14154 features across 2832 samples within 1 assay
## Active assay: RNA (14154 features, 3000 variable features)
## 2 dimensional reductions calculated: RNA_pca, RNA_pca_20_tsne
We normalize the count matrix for remaining cells :
sobj = Seurat::NormalizeData(sobj,
normalization.method = "LogNormalize",
assay = "RNA")
sobj = Seurat::FindVariableFeatures(sobj,
assay = "RNA",
nfeatures = 3000)
sobj
## An object of class Seurat
## 14154 features across 2832 samples within 1 assay
## Active assay: RNA (14154 features, 3000 variable features)
## 2 dimensional reductions calculated: RNA_pca, RNA_pca_20_tsne
We perform a PCA :
sobj = aquarius::dimensions_reduction(sobj = sobj,
assay = "RNA",
reduction = "pca",
max_dims = 100,
verbose = FALSE)
Seurat::ElbowPlot(sobj, ndims = 100, reduction = "RNA_pca")
We generate a tSNE and a UMAP with 20 principal components :
ndims = 20
sobj = Seurat::RunTSNE(sobj,
reduction = "RNA_pca",
dims = 1:ndims,
seed.use = 1337L,
reduction.name = paste0("RNA_pca_", ndims, "_tsne"),
check_duplicates = FALSE)
sobj = Seurat::RunUMAP(sobj,
reduction = "RNA_pca",
dims = 1:ndims,
seed.use = 1337L,
reduction.name = paste0("RNA_pca_", ndims, "_umap"))
We annotate cells for cell type, with the new normalized expression matrix :
score_columns = grep(x = colnames(sobj@meta.data), pattern = "^score", value = TRUE)
sobj@meta.data[, score_columns] = NULL
sobj$cell_type = NULL
sobj = aquarius::cell_annot_custom(sobj,
newname = "cell_type",
markers = cell_markers,
use_negative = TRUE,
add_score = TRUE,
verbose = TRUE)
sobj$cell_type = factor(sobj$cell_type, levels = names(cell_markers))
colnames(sobj@meta.data) = stringr::str_replace_all(string = colnames(sobj@meta.data),
pattern = " ",
replacement = "_")
table(sobj$cell_type)
##
## CD4 T cells CD8 T cells Langerhans cells macrophages
## 119 142 112 235
## B cells cuticle cortex medulla
## 262 135 203 87
## IRS proliferative IBL ORS
## 413 93 311 89
## IFE HFSC melanocytes sebocytes
## 316 216 57 42
(Time to run : 1.48 s)
To justify cell type annotation, we can make a dotplot :
markers = c("PTPRC", unique(unlist(dotplot_markers[levels(sobj$cell_type)])))
markers = markers[markers %in% rownames(sobj)]
aquarius::plot_dotplot(sobj, assay = "RNA",
column_name = "cell_type",
markers = markers,
nb_hline = 0) +
ggplot2::scale_color_gradientn(colors = aquarius:::color_gene) +
ggplot2::theme(legend.position = "right",
legend.box = "vertical",
legend.direction = "vertical",
axis.title = element_blank(),
axis.text = element_text(size = 15))
We can make a barplot to see the composition of each dataset, and visualize cell types on the projection.
df_proportion = as.data.frame(prop.table(table(sobj$orig.ident,
sobj$cell_type)))
colnames(df_proportion) = c("orig.ident", "cell_type", "freq")
quantif = table(sobj$orig.ident) %>%
as.data.frame.table() %>%
`colnames<-`(c("orig.ident", "nb_cells"))
# Plot
plot_list = list()
plot_list[[2]] = aquarius::plot_barplot(df = df_proportion,
x = "orig.ident",
y = "freq",
fill = "cell_type",
position = ggplot2::position_fill()) +
ggplot2::scale_fill_manual(name = "Cell type",
values = color_markers[levels(df_proportion$cell_type)],
breaks = levels(df_proportion$cell_type)) +
ggplot2::geom_label(data = quantif, inherit.aes = FALSE,
aes(x = orig.ident, y = 1.05, label = nb_cells),
label.size = 0)
plot_list[[1]] = Seurat::DimPlot(sobj, group.by = "cell_type",
reduction = "RNA_pca_20_tsne") +
ggplot2::scale_color_manual(values = unlist(color_markers),
breaks = names(color_markers)) +
ggplot2::labs(title = sample_name,
subtitle = paste0(ncol(sobj), " cells")) +
Seurat::NoLegend() + Seurat::NoAxes() +
ggplot2::theme(aspect.ratio = 1,
plot.title = element_text(hjust = 0.5),
plot.subtitle = element_text(hjust = 0.5))
patchwork::wrap_plots(plot_list, nrow = 1, widths = c(6, 1))
We annotate cells for cell cycle phase :
cc_columns = aquarius::add_cell_cycle(sobj = sobj,
assay = "RNA",
species_rdx = "hs",
BPPARAM = cl)@meta.data[, c("Seurat.Phase", "Phase")]
##
## G1 G2M S
## 928 455 230
sobj$Seurat.Phase = cc_columns$Seurat.Phase
sobj$cyclone.Phase = cc_columns$Phase
table(sobj$Seurat.Phase, sobj$cyclone.Phase)
##
## G1 G2M S
## G1 575 296 178
## G2M 137 93 16
## S 211 64 34
## Undecided 5 2 2
(Time to run : 83.84 s)
We visualize cell cycle on the projection :
plot_list = list()
plot_list[[2]] = Seurat::DimPlot(sobj, group.by = "Seurat.Phase",
reduction = "RNA_pca_20_tsne") +
ggplot2::labs(title = "Cell Cycle Phase",
subtitle = "Seurat.Phase") +
Seurat::NoLegend() + Seurat::NoAxes() +
ggplot2::theme(aspect.ratio = 1,
plot.title = element_text(hjust = 0.5),
plot.subtitle = element_text(hjust = 0.5))
plot_list[[1]] = Seurat::DimPlot(sobj, group.by = "cyclone.Phase",
reduction = "RNA_pca_20_tsne") +
ggplot2::labs(title = "Cell Cycle Phase",
subtitle = "cyclone.Phase") +
Seurat::NoLegend() + Seurat::NoAxes() +
ggplot2::theme(aspect.ratio = 1,
plot.title = element_text(hjust = 0.5),
plot.subtitle = element_text(hjust = 0.5))
patchwork::wrap_plots(plot_list, nrow = 1)
We make a highly resolutive clustering :
sobj = Seurat::FindNeighbors(sobj, reduction = "RNA_pca", dims = c(1:ndims))
sobj = Seurat::FindClusters(sobj, resolution = 2)
## Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
##
## Number of nodes: 2832
## Number of edges: 105289
##
## Running Louvain algorithm...
## Maximum modularity in 10 random starts: 0.4431
## Number of communities: 19
## Elapsed time: 0 seconds
table(sobj$seurat_clusters)
##
## 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18
## 312 309 259 200 199 198 193 186 161 137 111 109 103 76 75 72 66 63 3
We can visualize the cell type :
tsne = Seurat::DimPlot(sobj, group.by = "cell_type",
reduction = paste0("RNA_pca_", ndims, "_tsne"), cols = color_markers) +
Seurat::NoAxes() + ggplot2::ggtitle("tSNE") +
ggplot2::theme(aspect.ratio = 1,
plot.title = element_text(hjust = 0.5),
legend.position = "none")
umap = Seurat::DimPlot(sobj, group.by = "cell_type",
reduction = paste0("RNA_pca_", ndims, "_umap"), cols = color_markers) +
Seurat::NoAxes() + ggplot2::ggtitle("UMAP") +
ggplot2::theme(aspect.ratio = 1,
plot.title = element_text(hjust = 0.5))
tsne | umap
We can visualize the cell cycle, from Seurat :
tsne = Seurat::DimPlot(sobj, group.by = "Seurat.Phase",
reduction = paste0("RNA_pca_", ndims, "_tsne")) +
Seurat::NoAxes() + ggplot2::ggtitle("tSNE") +
ggplot2::theme(aspect.ratio = 1,
plot.title = element_text(hjust = 0.5),
legend.position = "none")
umap = Seurat::DimPlot(sobj, group.by = "Seurat.Phase",
reduction = paste0("RNA_pca_", ndims, "_umap")) +
Seurat::NoAxes() + ggplot2::ggtitle("UMAP") +
ggplot2::theme(aspect.ratio = 1,
plot.title = element_text(hjust = 0.5))
tsne | umap
We can visualize the cell cycle, from cyclone :
tsne = Seurat::DimPlot(sobj, group.by = "cyclone.Phase",
reduction = paste0("RNA_pca_", ndims, "_tsne")) +
Seurat::NoAxes() + ggplot2::ggtitle("tSNE") +
ggplot2::theme(aspect.ratio = 1,
plot.title = element_text(hjust = 0.5),
legend.position = "none")
umap = Seurat::DimPlot(sobj, group.by = "cyclone.Phase",
reduction = paste0("RNA_pca_", ndims, "_umap")) +
Seurat::NoAxes() + ggplot2::ggtitle("UMAP") +
ggplot2::theme(aspect.ratio = 1,
plot.title = element_text(hjust = 0.5))
tsne | umap
We visualize the clustering :
tsne = Seurat::DimPlot(sobj, group.by = "seurat_clusters", label = TRUE,
reduction = paste0("RNA_pca_", ndims, "_tsne")) +
Seurat::NoAxes() + ggplot2::ggtitle("tSNE") +
ggplot2::theme(aspect.ratio = 1,
plot.title = element_text(hjust = 0.5),
legend.position = "none")
umap = Seurat::DimPlot(sobj, group.by = "seurat_clusters", label = TRUE,
reduction = paste0("RNA_pca_", ndims, "_umap")) +
Seurat::NoAxes() + ggplot2::ggtitle("UMAP") +
ggplot2::theme(aspect.ratio = 1,
plot.title = element_text(hjust = 0.5))
tsne | umap
We visualize all cell types markers on the tSNE :
markers = dotplot_markers %>% unlist() %>% unname()
markers = markers[markers %in% rownames(sobj)]
plot_list = lapply(markers,
FUN = function(one_gene) {
p = Seurat::FeaturePlot(sobj, features = one_gene,
reduction = paste0("RNA_pca_", ndims, "_tsne")) +
ggplot2::labs(title = one_gene) +
ggplot2::scale_color_gradientn(colors = aquarius::color_gene) +
ggplot2::theme(aspect.ratio = 1,
plot.subtitle = element_text(hjust = 0.5)) +
Seurat::NoAxes()
return(p)
})
patchwork::wrap_plots(plot_list, ncol = 4)
We save the annotated and filtered Seurat object :
saveRDS(sobj, file = paste0(out_dir, "/datasets/", sample_name, "_sobj_filtered.rds"))
## R version 3.6.3 (2020-02-29)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 20.04.6 LTS
##
## Matrix products: default
## BLAS: /usr/local/lib/R/lib/libRblas.so
## LAPACK: /usr/local/lib/R/lib/libRlapack.so
##
## locale:
## [1] C
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] ggplot2_3.3.5 patchwork_1.1.2 dplyr_1.0.7
##
## loaded via a namespace (and not attached):
## [1] softImpute_1.4 graphlayouts_0.7.0
## [3] pbapply_1.4-2 lattice_0.20-41
## [5] haven_2.3.1 vctrs_0.3.8
## [7] usethis_2.0.1 dynwrap_1.2.1
## [9] blob_1.2.1 survival_3.2-13
## [11] prodlim_2019.11.13 dynutils_1.0.5
## [13] later_1.3.0 DBI_1.1.1
## [15] R.utils_2.11.0 SingleCellExperiment_1.8.0
## [17] rappdirs_0.3.3 uwot_0.1.8
## [19] dqrng_0.2.1 jpeg_0.1-8.1
## [21] zlibbioc_1.32.0 pspline_1.0-18
## [23] pcaMethods_1.78.0 mvtnorm_1.1-1
## [25] htmlwidgets_1.5.4 GlobalOptions_0.1.2
## [27] future_1.22.1 UpSetR_1.4.0
## [29] laeken_0.5.2 leiden_0.3.3
## [31] clustree_0.4.3 parallel_3.6.3
## [33] scater_1.14.6 irlba_2.3.3
## [35] DEoptimR_1.0-9 tidygraph_1.1.2
## [37] Rcpp_1.0.9 readr_2.0.2
## [39] KernSmooth_2.23-17 carrier_0.1.0
## [41] promises_1.1.0 gdata_2.18.0
## [43] DelayedArray_0.12.3 limma_3.42.2
## [45] graph_1.64.0 RcppParallel_5.1.4
## [47] Hmisc_4.4-0 fs_1.5.2
## [49] RSpectra_0.16-0 fastmatch_1.1-0
## [51] ranger_0.12.1 digest_0.6.25
## [53] png_0.1-7 sctransform_0.2.1
## [55] cowplot_1.0.0 DOSE_3.12.0
## [57] ggvenn_0.1.9 here_1.0.1
## [59] TInGa_0.0.0.9000 ggraph_2.0.3
## [61] pkgconfig_2.0.3 GO.db_3.10.0
## [63] DelayedMatrixStats_1.8.0 gower_0.2.1
## [65] ggbeeswarm_0.6.0 iterators_1.0.12
## [67] DropletUtils_1.6.1 reticulate_1.26
## [69] clusterProfiler_3.14.3 SummarizedExperiment_1.16.1
## [71] circlize_0.4.15 beeswarm_0.4.0
## [73] GetoptLong_1.0.5 xfun_0.35
## [75] bslib_0.3.1 zoo_1.8-10
## [77] tidyselect_1.1.0 reshape2_1.4.4
## [79] purrr_0.3.4 ica_1.0-2
## [81] pcaPP_1.9-73 viridisLite_0.3.0
## [83] rtracklayer_1.46.0 rlang_1.0.2
## [85] hexbin_1.28.1 jquerylib_0.1.4
## [87] dyneval_0.9.9 glue_1.4.2
## [89] RColorBrewer_1.1-2 matrixStats_0.56.0
## [91] stringr_1.4.0 lava_1.6.7
## [93] europepmc_0.3 DESeq2_1.26.0
## [95] recipes_0.1.17 labeling_0.3
## [97] httpuv_1.5.2 class_7.3-17
## [99] BiocNeighbors_1.4.2 DO.db_2.9
## [101] annotate_1.64.0 jsonlite_1.7.2
## [103] XVector_0.26.0 bit_4.0.4
## [105] mime_0.9 aquarius_0.1.5
## [107] Rsamtools_2.2.3 gridExtra_2.3
## [109] gplots_3.0.3 stringi_1.4.6
## [111] processx_3.5.2 gsl_2.1-6
## [113] bitops_1.0-6 cli_3.0.1
## [115] batchelor_1.2.4 RSQLite_2.2.0
## [117] randomForest_4.6-14 tidyr_1.1.4
## [119] data.table_1.14.2 rstudioapi_0.13
## [121] org.Mm.eg.db_3.10.0 GenomicAlignments_1.22.1
## [123] nlme_3.1-147 qvalue_2.18.0
## [125] scran_1.14.6 locfit_1.5-9.4
## [127] scDblFinder_1.1.8 listenv_0.8.0
## [129] ggthemes_4.2.4 gridGraphics_0.5-0
## [131] R.oo_1.24.0 dbplyr_1.4.4
## [133] BiocGenerics_0.32.0 TTR_0.24.2
## [135] readxl_1.3.1 lifecycle_1.0.1
## [137] timeDate_3043.102 ggpattern_0.3.1
## [139] munsell_0.5.0 cellranger_1.1.0
## [141] R.methodsS3_1.8.1 proxyC_0.1.5
## [143] visNetwork_2.0.9 caTools_1.18.0
## [145] codetools_0.2-16 Biobase_2.46.0
## [147] GenomeInfoDb_1.22.1 vipor_0.4.5
## [149] lmtest_0.9-38 msigdbr_7.5.1
## [151] htmlTable_1.13.3 triebeard_0.3.0
## [153] lsei_1.2-0 xtable_1.8-4
## [155] ROCR_1.0-7 BiocManager_1.30.10
## [157] scatterplot3d_0.3-41 abind_1.4-5
## [159] farver_2.0.3 parallelly_1.28.1
## [161] RANN_2.6.1 askpass_1.1
## [163] GenomicRanges_1.38.0 RcppAnnoy_0.0.16
## [165] tibble_3.1.5 ggdendro_0.1-20
## [167] cluster_2.1.0 future.apply_1.5.0
## [169] Seurat_3.1.5 dendextend_1.15.1
## [171] Matrix_1.3-2 ellipsis_0.3.2
## [173] prettyunits_1.1.1 lubridate_1.7.9
## [175] ggridges_0.5.2 igraph_1.2.5
## [177] RcppEigen_0.3.3.7.0 fgsea_1.12.0
## [179] remotes_2.4.2 scBFA_1.0.0
## [181] destiny_3.0.1 VIM_6.1.1
## [183] testthat_3.1.0 htmltools_0.5.2
## [185] BiocFileCache_1.10.2 yaml_2.2.1
## [187] utf8_1.1.4 plotly_4.9.2.1
## [189] XML_3.99-0.3 ModelMetrics_1.2.2.2
## [191] e1071_1.7-3 foreign_0.8-76
## [193] withr_2.5.0 fitdistrplus_1.0-14
## [195] BiocParallel_1.20.1 xgboost_1.4.1.1
## [197] bit64_4.0.5 foreach_1.5.0
## [199] robustbase_0.93-9 Biostrings_2.54.0
## [201] GOSemSim_2.13.1 rsvd_1.0.3
## [203] memoise_2.0.0 evaluate_0.18
## [205] forcats_0.5.0 rio_0.5.16
## [207] geneplotter_1.64.0 tzdb_0.1.2
## [209] caret_6.0-86 ps_1.6.0
## [211] DiagrammeR_1.0.6.1 curl_4.3
## [213] fdrtool_1.2.15 fansi_0.4.1
## [215] highr_0.8 urltools_1.7.3
## [217] xts_0.12.1 GSEABase_1.48.0
## [219] acepack_1.4.1 edgeR_3.28.1
## [221] checkmate_2.0.0 scds_1.2.0
## [223] cachem_1.0.6 npsurv_0.4-0
## [225] babelgene_22.3 rjson_0.2.20
## [227] openxlsx_4.1.5 ggrepel_0.9.1
## [229] clue_0.3-60 rprojroot_2.0.2
## [231] stabledist_0.7-1 tools_3.6.3
## [233] sass_0.4.0 nichenetr_1.1.1
## [235] magrittr_2.0.1 RCurl_1.98-1.2
## [237] proxy_0.4-24 car_3.0-11
## [239] ape_5.3 ggplotify_0.0.5
## [241] xml2_1.3.2 httr_1.4.2
## [243] assertthat_0.2.1 rmarkdown_2.18
## [245] boot_1.3-25 globals_0.14.0
## [247] R6_2.4.1 Rhdf5lib_1.8.0
## [249] nnet_7.3-14 RcppHNSW_0.2.0
## [251] progress_1.2.2 genefilter_1.68.0
## [253] statmod_1.4.34 gtools_3.8.2
## [255] shape_1.4.6 HDF5Array_1.14.4
## [257] BiocSingular_1.2.2 rhdf5_2.30.1
## [259] splines_3.6.3 AUCell_1.8.0
## [261] carData_3.0-4 colorspace_1.4-1
## [263] generics_0.1.0 stats4_3.6.3
## [265] base64enc_0.1-3 dynfeature_1.0.0
## [267] smoother_1.1 gridtext_0.1.1
## [269] pillar_1.6.3 tweenr_1.0.1
## [271] sp_1.4-1 ggplot.multistats_1.0.0
## [273] rvcheck_0.1.8 GenomeInfoDbData_1.2.2
## [275] plyr_1.8.6 gtable_0.3.0
## [277] zip_2.2.0 knitr_1.41
## [279] ComplexHeatmap_2.14.0 latticeExtra_0.6-29
## [281] biomaRt_2.42.1 IRanges_2.20.2
## [283] fastmap_1.1.0 ADGofTest_0.3
## [285] copula_1.0-0 doParallel_1.0.15
## [287] AnnotationDbi_1.48.0 vcd_1.4-8
## [289] babelwhale_1.0.1 openssl_1.4.1
## [291] scales_1.1.1 backports_1.2.1
## [293] S4Vectors_0.24.4 ipred_0.9-12
## [295] enrichplot_1.6.1 hms_1.1.1
## [297] ggforce_0.3.1 Rtsne_0.15
## [299] shiny_1.7.1 numDeriv_2016.8-1.1
## [301] polyclip_1.10-0 grid_3.6.3
## [303] lazyeval_0.2.2 Formula_1.2-3
## [305] tsne_0.1-3 crayon_1.3.4
## [307] MASS_7.3-54 pROC_1.16.2
## [309] viridis_0.5.1 dynparam_1.0.0
## [311] rpart_4.1-15 zinbwave_1.8.0
## [313] compiler_3.6.3 ggtext_0.1.0